Methods for spatially relative apportionment of geospatial data in distributed computing systems, and related systems and apparatus

ABSTRACT

A method and processing system for assessing of the health of a network of retail locations in a defined geographic area, by a process including the development of a greenfield benchmark score for a network of ideally sited locations, followed by the development of a brownfield score of current locations, for comparison to the greenfield score. The invention is further applicable to decisions to open a new location, relocate an existing location or close an existing location. In these cases, the effect of opening a new location, or relocating or closing an underperforming location is evaluated by recalculating the brownfield score after the addition of each of several potential new locations, and/or after relocating or closing each of several poorly performing locations.

RELATED APPLICATION

This application is a non-provisional application of and claims priorityto U.S. Provisional Application Ser. No. 62/595,913 filed Dec. 7, 2017,which is incorporated herein by reference in its entirety.

FIELD OF THE INVENTION

The present invention relates to methods and systems for geospatialmarket analytics, and specifically the use of geospatial analytics forthe purpose of siting retail locations and other geographic decisions.

BACKGROUND OF THE INVENTION

Geospatial analysis has been known in the art. Methods have beenproposed for associating relevant marketing data to a geographical mapfor the purposes of evaluating market strength in a particular area.This type of analysis is typically performed as part of the siting of aretail location.

One proposal for a detailed geospatial analysis is described in USPatent Publication 2015/0073954, which explains a manner in which datafrom a financial institution is geographically mapped to build ageospatial database. The data used may be credit card or debit cardtransaction data associated with particular residential addresses. Thepublished method performs several heuristic processes to clean the dataand associate the data to particular geographic areas, as well asanonymizing the data to safeguard the privacy of individual card andaccount holders. The data is used by business clients for the purposesof evaluating decisions as to whether to make promotions, investmentsand other transactions in particular geographic areas.

A challenge with these known systems is that the geographic granularityis not sufficient to enable decision making at the accuracy that can beneeded for some applications. This is a consequence of the fact that theraw data which can feed into a geospatial database is often provided ata low level of granularity. As one instance some demographic data isprovided on a ZIP code basis; in some urban areas a ZIP code region canbe rather small, but in rural areas one ZIP code can span hundreds ofsquare miles, thus limiting the granularity of data in such areas.

It is an object of the present invention to improve the granularity of ageospatial database to then allow much more particularizeddecisionmaking than has previously been the case, for example to providedecisionmaking on geographic areas of a well under one square mile. Thispermits “streetcorner” decisionmaking and allows the relative grading oftwo locations which in prior systems would have been in a common regionand indistinguishable from one another.

It is a further object of the invention to enhance the use of ageospatial database, particularly one with the noted enhanced detail, topermit additional improvements in the management and decisionmaking of aretail or other consumer facing business.

SUMMARY OF THE INVENTION

The present invention generates a geospatial database at a high level ofgranularity via several algorithms for allocating geospatial data tosmaller geographic areas than those provided by the raw data.

The present invention further provides a method for assessment of thehealth of a network of retail locations in a defined geographic area, bya process including the development of a greenfield benchmark score fora network of ideally sited locations, followed by the development of abrownfield score of current locations, for comparison to the greenfieldscore. In some embodiments the comparison is expressed as a percentageratio of the brownfield score to the greenfield score. In detailedembodiments the greenfield score is formed by siting a first retaillocation at the highest scoring location in an area, degrading thescores of surrounding areas to reflect the presence of the first retaillocation, then repeating these steps until a designated number of retailsites are chosen. A brownfield score is formed by identifying thehighest scoring existing location and including it as the first locationin the brownfield score, then degrading the scores of surrounding areasto reflect the presence of the first location, and the identifying thenext highest scoring location and including it as the second location inthe brownfield score, and repeating these steps until all of theexisting locations are included in the brownfield score. A comparison ofthe thus-developed greenfield and brownfield scores provides a robustassessment of a network's health.

In another aspect the invention is applicable to decisions to open a newlocation, relocate an existing location or close an existing location.In these cases, the effect of opening a new location, or relocating orclosing an underperforming location is evaluated by recalculating thebrownfield score after the addition of each of several potential newlocations, and/or after relocating or closing each of several poorlyperforming locations. In specific embodiments of this aspect, customeractivity data is used to identify locations which are used by commoncustomers and evaluate the network effect of closing one of thoselocations upon the customers who use both locations. In furtherembodiments, customer activity data is used to identify locations thatare used by numerous common customers with multiple other locations,which are known as “hub” locations, and to respond by expanding accessto services at a “hub” location such as by changing hours of business orlocating specialized talent or resources at that location. In stillfurther embodiments customer activity data is used to identify healthylocations which do not have common customers with other locations, astargets for potential divestiture rather than closure.

In additional aspects the invention is applicable to evaluatingpotential retail partners by assessing the retail locations of potentialpartners according to a brownfield scoring analysis that includesexisting retail locations, to determine which of several potentialpartners provides the greatest value of new retail locations.

In still further aspects the invention is applicable to evaluatingpotential merger or acquisition targets, by assessing the retaillocations of potentially merged entities according to a brownfieldscoring analysis that includes existing locations of both potentiallymerged entities.

The above and other objects and advantages of the present inventionshall be made apparent from the accompanying drawings and thedescription thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a computer system for performing ageographic analysis consistent with the invention;

FIG. 2A is a flowchart illustrating the overall process implemented bythe computer system of FIG. 1 to tessellate a geographic area intoappropriate hexes for the purpose of geospatial market analysis inaccordance with the present invention;

FIG. 2B is a flow chart for initializing a hex grid by a tessellationprocess, then limiting the hexes through intersection with streetnetwork and state boundary data;

FIG. 2C is a flowchart illustrating the use of a drive time algorithmfor generating an origin-destination table of drive times applicable tohexes which intersect the street network;

FIG. 2D is a flowchart illustrating the apportionment of data fromgeographic input files of spatially relative data to tessellatedgeographic areas through the use of drive time, to build flattened datafor future modeling and evaluation;

FIG. 2E is a flowchart illustrating the handling of hexes that do notintersect roads to identify nearby hexes applicable thereto;

FIG. 2F is a flowchart generally illustrating a site selection processthat proceeds from the generated data using a site selection rule set;

FIG. 2G is a flowchart illustrating a process for identifying micronetworks from hex data, transaction data, and retail locations, for usein further processing and geospatial analysis;

FIGS. 3A, 3B, 3C and 3D illustrate tessellation, formation of hexesidentifiable by hex centroids, identification of hexes that intersectroads, and integration and apportionment of geographical data;

FIGS. 4A, 4B, 4C, 4D and 4E illustrate the creation of Thiessen/Voronipolygons around existing retail sites and the intersection of thosepolygons using micronetworks; and

FIG. 5 illustrates a greenfield and brownfield benchmarking processusing the geographic data developed in accordance with principles of thepresent invention.

DETAILED DESCRIPTION OF THE INVENTION

Turning now to the Drawings, wherein like numbers denote like partsthroughout the several views, FIG. 1 illustrates an exemplary hardwareand software environment for a processing apparatus 2 consistent withthe invention. For the purposes of the invention, processing apparatus 2(or “processor”) may represent practically any type of computer,computer system or other programmable electronic device, including aclient computer, a server computer, a personal computer, a portablecomputer, a handheld computer, an embedded controller, etc. Moreover,processor 2 may be implemented using one or more networked computers,e.g., in a cluster or other distributed computing system. Processor 2may be capable of functioning as a client and/or server in aclient-server environment. Moreover, processor 2 may be capable offunctioning as a client and/or server in a peer-to-peer environment.Multiple processors 2 may be interfaced in a client-server environmentand/or peer-to peer environment. Processor 2 will hereinafter also bereferred to as a “computer,” although it should be appreciated that theterm “processor” may also include other suitable programmable electronicdevices consistent with the invention.

Computer 2 typically includes a central processing unit (CPU) 4including one or more microprocessors coupled to a memory 6, which mayrepresent the random access memory (RAM) devices comprising the mainstorage of computer 2, as well as any supplemental levels of memory,e.g., cache memories, non-volatile or backup memories (e.g.,programmable or flash memories, solid state or disk memory), read-onlymemories, etc. In addition, memory 6 may be considered to include memorystorage physically located elsewhere in computer 2, e.g., any cachememory in a processor in CPU 4, as well as any storage capacity used asa virtual memory, e.g., as stored on a mass storage device 14 or onanother computer coupled to computer 2.

Computer 2 also typically receives a number of inputs and outputs forcommunicating information externally. For interface with a user oroperator, computer 2 typically includes a user interface 20 and/or aninput/output interface 22 incorporating one or more user input/outputdevices 24 (e.g., a keyboard 28, a mouse 30, a printer 32, a trackball,a joystick, a touchpad, and/or a microphone, among others) and a display26 (e.g., a CRT monitor, an LCD display panel, and/or a speaker, amongothers). Otherwise, user input may be received via another computer orterminal, e.g., via a client or single-user computer 40 coupled tocomputer 2 over a network 36. This latter implementation may bedesirable where computer 2 is implemented as a server or other form ofmulti-user computer. However, it should be appreciated that computer 2may also be implemented as a standalone workstation, desktop, or othersingle-user computer in some embodiments.

For non-volatile storage, computer 2 typically includes one or more massstorage devices 14, e.g., a floppy or other removable disk drive, a harddisk drive, a direct access storage device (DASD), an optical drive(e.g., a CD drive, a DVD drive, etc.), and/or a tape drive, amongothers. Furthermore, computer 2 may also include an interface 34 withone or more networks 36 (e.g., a LAN, a WAN, a wireless network, and/orthe Internet, among others) to permit the communication of informationwith other computers and electronic devices. It should be appreciatedthat computer 2 typically includes suitable analog and/or digitalinterfaces between CPU 4 and each of components 6, 14, 34, and 20 as iswell known in the art (e.g., via bus 18).

Computer 2 operates under the control of an operating system 12, andexecutes or otherwise relies upon various computer softwareapplications, components, programs, objects, modules, data structures,etc. Additionally, various applications, components, programs, object,modules, etc. may also execute on one or more processors in anothercomputer coupled to computer 2 via a network, e.g., in a distributed orclient-server computing environment, whereby the processing required toimplement the functions of a computer program may be allocated tomultiple computers over a network.

In particular, a Geospatial Analysis System 8 may be resident in memory6 and used to access a Geographical Database 16 resident in mass storage14. System 8 may be used to evaluate a geographical area and variousgeographical information to create a tessellation and hex pattern forthe geographical area that reflects some or all of the availablegeographic information. Additionally, System 8 may be used by a user toevaluate geographic data in Database 16 as well as retrieve data fromDatabase 16. Database 16 may also be accessible by the operating system12.

The Geospatial Analysis System 8 may also have a Benchmarkingapplication 10 associated with it, providing the user the ability tocreate benchmarking analysis of geographic area and retail locationsplaced therein, as described below.

In general, the routines executed to implement the embodiments of theinvention, whether implemented as part of an operating system or aspecific application, component, program, object, module or sequence ofinstructions, or even a subset thereof, will be referred to herein as“computer program code,” or simply “program code.” Program codetypically comprises one or more instructions that are resident atvarious times in various memory and storage devices in a computer, andthat, when read and executed by one or more processors in a computer,cause that computer to perform the steps necessary to execute steps orelements embodying the various aspects of the invention.

Moreover, while the invention has and hereinafter will be described inthe context of fully functioning computers and computer systems, thoseskilled in the art will appreciate that the various embodiments of theinvention are capable of being distributed as a program product in avariety of forms, and that the invention applies equally regardless ofthe particular type of computer readable media used to actually carryout the distribution. Examples of computer readable media include butare not limited to tangible, recordable type media such as volatile andnon-volatile memory devices, floppy and other removable disks, hard diskdrives, magnetic tape, optical disks (e.g., CD-ROMs, DVDs, etc.), amongothers, and transmission type media such as digital and analogcommunication links.

In addition, various program code described hereinafter may beidentified based upon the application within which it is implemented ina specific embodiment of the invention. However, it should beappreciated that any particular program nomenclature that follows isused merely for convenience, and thus the invention should not belimited to use solely in any specific application identified and/orimplied by such nomenclature. Furthermore, given the typically endlessnumber of manners in which computer programs may be organized intoroutines, procedures, methods, modules, objects, and the like, as wellas the various manners in which program functionality may be allocatedamong various software layers that are resident within a typicalcomputer (e.g., operating systems, libraries, API's, applications,applets, etc.), it should be appreciated that the invention is notlimited to the specific organization and allocation of programfunctionality described herein.

Those skilled in the art will recognize that the exemplary environmentillustrated in FIG. 1 is not intended to limit the present invention.Indeed, those skilled in the art will recognize that other alternativehardware and/or software environments may be used without departing fromthe scope of the invention.

Referring now to FIG. 2A, a geospatial market analysis method performedby a processor such as discussed above, involves a series of geographicprocessing steps, some of which initialize the system for later analysisand are infrequently repeated, and some of which are repeated each timethe system is used for a new analysis.

In a first step 201, the processor performs a tessellation of the entiregeographic area of interest, to produce an end-to-end hex grid 208. Theresulting grid of hexagonal areas or “hexes”, illustrated in FIG. 3A,serves as a baseline for subsequent geographic functions. The grid ofhexes is typically created only one time and used thereafter, and eachis identified by a centroid point as shown in FIG. 3B.

Next, the hexes created by the preceding step are analyzed in two waysto limit the number of hexes analyzed. First, in step 202, the processoroverlays the street network for the area in interest on the hexes andselects those hexes which lie on (aka accessible via) the streetnetwork. The overlay of the street network is seen graphically in FIG.3C, and hexes on the street network are shaded in FIG. 3C forillustration. As seen in FIG. 2E, this process involves the use of astreet network database 260 and hex grid 261 for the region of interest,for example a grid of the continental United States. Those hexes arewithin 0.16 miles of hexes that intersect roads are included 262 in agroup of “near hexes” 263, which are read and assigned 264 to their nextclosest hex for the sale of identifying those hexes deemed accessible inthe output table 265.

In addition to the above processes, because state or other politicalboundaries tend to affect the street network, in step 209 those hexeswithin 20 miles of state boundaries are selected for further processing.The resulting hexes are then available for combination 204 withgeographical data 211.

After the foregoing, a drive time algorithm 203 is applied to theselected hexes, to produce an origin-designation table 210 providingdrive times from one hex to another, useful for subsequent processing.As seen in FIG. 2C, the drive time process is performed for thosehexagons 240 that intersect the street network, which is known fromavailable databases 242. The hex data and street network data are read241, 243, and then an algorithm, of which there are several commerciallyavailable alternatives, is used to compute drive times between hexesusing one of several rule sets 245 relating to assumed trafficconditions, driving times, and the like. The algorithm may operate froma rule set to compute drive times, including factors such as whether theadjacent streets are one way roads, public or private, the functionalclass of the roadway and the speed limit of the roadway. Other data suchas historical traffic may also feed into drive time calculation rules.The resulting origin-destination table is the needed output for thefollowing geographical analysis steps and is re-generated to accommodatefor changes in the street network, or traffic patterns, as necessary.Typically, a regeneration will be required at least every three to fiveyears to accommodate for street network changes.

As seen in FIGS. 2A and 2B, the result of the tessellation 230 isdelivered to three processes: first, a process 231 which identifies thehexes having intersection with the street network, a process 232 whichselects hexes within 20 miles of state boundaries, and a process 233which combines the hexes with geographical data. The intersection withgeographical data involves apportionment of several types, as discussedherein.

As seen in FIG. 2D, input files 250 containing geographic data, are read251, and combined with the origin-destination matrix 252 produced in thepreceding step, which is read 253 and supplied to an apportionmentprocess 254 which utilizes drive times. In this manner, ZIP code datacan be apportioned to all of the hexes bordering that ZIP in proportionto the relative drive time of each hex to the ZIP code. This is showndiagrammatically in FIG. 3D where data for a region (shown by a whiteoutline) which applies to a group of locations is allocated toindividual hexes, and at the same time data relating to points (squares,circles) lying within a single hex are allocated to that hex. Data onincome, employment, credit ratings or any other factor which is indexedby household, or averages for a city, state, ZIP code, neighborhood orthe like can be similarly apportioned. Each contributes to a scoring fora hex based upon the desirability of the apportioned data to the retailpurpose. After the data is accumulated, the data is flattened 255 to asingle score which is tabulated 256 and useable for analysis.

The market data can include numerous sources, such as financial activitydata (credit or debit card transactions, loan activity, auto or hometitle transfers, and the like) or financial asset data (investment orbanking account data). The data from larger regions is mapped into thesmaller regions by attributing to each smaller region any dataassociated with locations within a given drive time of that region,using available road mapping databases and drive time algorithms, or byallocating more of the data to a region.

Returning to FIG. 2A, after geographic data is intersected 251 andapportioned 206, which results in apportioned data tables 212 via thedescribed process 213, models based upon infrastructures attributes aredeveloped 207, and used in connection with hexes 215 which do notintersect roads, to create scoring data which is combinable with theapportioned data tables 217. The infrastructure models approximate theaccessibility of locations which are near to existing infrastructureeven if not yet accessible by an intersection part of the road network.

The resulting two sets of data, one for hexes intersecting roads and onefor hexes near to roads, are imported 216 and combined with models forscoring locations 214, using real time algorithms 218 to create anoverall scoring for every location in a geographic region. As seen inFIG. 2F, this process uses the origin-destination matrix 270 formedearlier and the model scores 271, which are read 272, and used with asite selection rule set 274 to select 273 an optimal site or combinationof sites, as discussed below referencing FIG. 5. This produces a set ofselected sites 275 which may be mapped 276. As seen in FIG. 2A, thescores are processed through a symbology metric 219, to create a mappingoutput 219 which may take the form of a physical printout 221 or adigital map 222 for display.

This part of the process of FIG. 2A operates in real time, allowingadjustment of the model scores 214 and re-evaluation of the hexdatabases in order to refine the scoring of locations based upon therelative importance of the various scoring factors involved in thescoring.

Referring now to FIG. 2G, a process for identifying micro networks isdescribed. In this process, retail locations 280 are combined with hexdata 281 developed in accordance with the foregoing steps and matchedwith transaction data 282 from a common customer database, such as theregisters of transactions at a consumer banking entity. These files areread collectively 283 and two parallel analyses are performed.

First, the geographic region populated by the existing retail locations(dots in FIG. 4A) is subdivided by generating 285 Thiessen polygonsaround each location (polygons in the right on FIG. 4A). This knownprocess involves taking the intersection of polygons created byperpendicular bisectors of line segments connecting each location to itsneighbors. The resulting polygons seen in FIG. 4A each represent oneretail locations location geographic share of the entire region ofinterest.

Second, the transaction data is converted 284 to a spatial networkdiagram, such as those shown in FIGS. 4B and 4C. As seen in FIG. 4B, ageographic area is captured, based on a path (left side of FIG. 4B)between each pair of retail locations that are associated or correlatedto each other in the transaction data via common customer activity. Thegeographic area is defined as the path surrounded with a buffer regionof, e.g., 2 meters, to create a geographic representation of eachconnection (right side of FIG. 4B). Next, as seen in FIG. 4C, theoverlapping geographic representations (left side of FIG. 4B) are mergedto create a geographic feature (right side of FIG. 4C) representing thegeographic span of an economically connected set of retail locations.

After the preceding steps, the processor intersects the network diagramfrom FIG. 4C with the Thiessen polygons of FIG. 4A, to overlap each asseen in the right side of FIG. 4D, and next, as seen in the right sideof FIG. 4E, the processor dissolves the intersecting polygons to producecombined polygons representing micronetworks of retail locations whichare physically adjacent and economically coupled, which are known hereinas micronetworks.

The appendix attached to U.S. Provisional Patent Application 62/595,913,filed Dec. 7, 2017, describes the use of commercial software fromEnvironmental Systems Research Institute, Inc. of 380 New York Street,Redlands, Calif. 92373-8100, United States, for implementing theherein-described algorithm used for populating a geospatial databasewith information at a high level of granularity for the purposes ofperforming analyses according to the present invention. Additionalalgorithms for developing a geospatial database are presented in theabove-referenced US Patent Publication 2015/0073954.

Referring now to FIG. 5, after development of a geospatial databaseaccording to the noted process, a benchmarking process in accordancewith the present invention proceeds according to the following sequenceof steps:

An initial, greenfield algorithm 500 is used for developing a benchmarkscore for a targeted region having a number of pre-existing retailsites. This is performed using a so-called “greedy” location selectionmethod 502 and a multi-site location method 512, which producecomparable strategies for later evaluation.

In the greedy location selection method 502, the processor searches 504for locations within the target region with sufficiently high scores tobe profitable, and eligible for a retail site (appropriate zoning).Next, in step 506 the highest scoring location of those meeting thecriteria of step 504 is selected. Thereafter, in step 508 the processordegrades the scoring of sites surrounding the chosen location based ontheir adjacency (e.g., driving time) to the site chosen in step 506.Thereafter, steps 506 and 508 are repeated until the targeted number ofsites have been identified in the targeted region.

In the greenfield algorithm for developing a benchmark score for atargeted region, multi-site location selection version 512, theprocessor searches 514 for locations within the target region withsufficiently high scores to be profitable, and eligible for a retailsite (appropriate zoning). Next, in step 516 the processor evaluatesgroups of two or more locations meeting the criteria of step 514 toidentify a group of locations having the highest combined score when thescores of each location are degraded based upon adjacency (e.g., drivingtime) to the other locations in the group. A group of locations isselected if their combined score is higher than the combined scores ofan equal number of locations placed according to the greedy version 502of the algorithm. After selection of groups through the preceding steps,the processor degrades the scoring of sites surrounding the chosenlocations based on their adjacency (e.g., driving time) to the siteschosen in step 516. Thereafter, steps 516 and 518 are repeated until thetargeted number of sites have been identified in the targeted region.

After the foregoing process of greenfield scoring of possible sites in agiven target region, in step 524 a brownfield scoring is performed forthe targeted region and the pre-existing retail sites, which involvesidentifying the highest scoring pre-existing retail site, degrading thescoring of pre-existing sites surrounding the selected pre-existing sitebased on their adjacency (e.g. driving time) to the chosen site chosenin step, and then repeating these steps until all pre-existing retailsites have been identified and scored.

To determine the overall health of an existing retail site network, thegreenfield and brownfield scores are compared. Specifically, in step 526the processor computes the ratio of the brownfield score from step 524to the greenfield score compiled by the preceding steps to create apercentage measure of the existing network health.

After thus computing a network score, then multiple further analyticalsteps may be performed.

For example, a computation of remaining capacity in a targeted regionmay proceed by applying the greenfield algorithms of 502 or 512 to theregion after degrading all location scores based on adjacency (e.g.driving time) to the pre-existing retail sites. Additional locations maythen be selected according to the greenfield algorithm of 502 or 512until any remaining available locations have insufficient scores forprofitability. This can provide a measure of whether a targeted regionhas been saturated with retail locations, and, if not, where additionalretail locations can be ideally located.

Further, an evaluation of locations may identify the potential gainsavailable from closing or relocating a retail location, by performingbrownfield evaluations (step 524) on existing locations without thelocation to be closed or relocated, and comparing the results to thebrownfield evaluation with that location present. Identification of asite for a new location can proceed by performing a greenfield analysisto identify the optimal location.

In those industries where the activities of consumers can be wellcharacterized, such as in banking or retail lines with robust loyaltyprograms, the choices of locations to be closed or relocated can be evenmore informed, by identifying those locations which are linked to eachother by common customers. Locations may be related to just one otherlocation, or locations may be linked to a group of other locations via aring or hub-spoke relationship, and locations may be isolated in thesense of not having many common customers with other locations. Applyingdata to identify these scenarios permits a more refined decision processin closing or relocating retail locations. For example, one location ofa closely linked pair of locations may be closed with less loss ofcustomers than would be otherwise predicted, and the most tightlyconnected spoke location of a group of locations in a hub-and-spokerelationship can likely be closed with less loss of customers than wouldbe otherwise predicted. Locations which are isolated present a concernfor loss of customers likely to exceed what would be otherwisepredicted, and thus can be considered candidates to be retained, or soldto another retail entity, rather than closed, to avoid loss of value.

While the present invention has been illustrated by a description ofvarious embodiments and while these embodiments have been described inconsiderable detail, it is not the intention of the applicants torestrict or in any way limit the scope of the appended claims to suchdetail. Additional advantages and modifications will readily appear tothose skilled in the art. The invention in its broader aspects istherefore not limited to the specific details, representative apparatusand method, and illustrative example shown and described. Accordingly,departures may be made from such details without departing from thespirit or scope of applicant's general inventive concept.

What is claimed is: 1-14. (canceled)
 15. A method for apportionment ofgeospatial data, the method comprising: receiving, from a data storagedevice, a dataset comprising tile data corresponding to a geographicalregion, wherein the tile data corresponds to a respective sub-regions ofthe geographical region and tile attribute records, and wherein the tiledata (i) uniquely identifies a respective tile and (ii) indicatesrespective sub-region attribute values of the sub-region correspondingto the respective tile; receiving, from a data storage device, spatialrelationship data, wherein the spatial relationship data (i) identifiesa respective pair of tiles included in the tile data and (ii) indicatesa spatial relationship between the respective pair of tiles; spatiallyapportioning the tile data based at least in part on the spatialrelationship data by one or more processors, wherein performing thespatially relative apportionment includes, for each tile T in the tiledata and each spatial relationship R between tiles, identifying, basedon the spatial relationship data, a subset of the tile data having therespective spatial relationship R to the respective tile T, for eachsub-region attribute, generating an aggregated attribute value for therespective attribute, generating a record of spatially relativeapportionment (i) identifying the respective tile T and the respectivespatial relationship R and (ii) indicating the aggregated attributevalues of the subset of tiles having the respective spatial relationshipR to the respective tile T.
 16. The method of claim 15, wherein thespatial relationship data is obtained prior to the performing of thespatially relative apportionment.
 17. The method of claim 15, furthercomprising: generating a second set of tile attribute data; andspatially apportioning, using one or more processors, the second set oftile attribute data set based on the spatial relationship data.
 18. Themethod of claim 15, further comprising assessing a quality of a set ofphysical locations of sites of a type of activity within a portion ofthe geographical region corresponding to a subset of the tiles.
 19. Themethod of claim 18, wherein assessing the quality of the set of physicallocations comprises: determining a greenfield benchmark score for theportion of the geographical region based on one or more potentiallocations of sites of the type of activity within the geographicalregion; determining a brownfield score for the portion of thegeographical region based on one or more current physical locations ofsites of the type of activity within the geographical region; anddetermining a quality score based on the brownfield score and thegreenfield benchmark score.
 20. The method of claim 19, whereindetermining the quality score comprises determining a ratio of thebrownfield score to the greenfield benchmark score.
 21. The method ofclaim 19, further comprising assessing an impact of adding a new site ofthe type of activity at a particular location within the portion of thegeographical region, comprising: determining an updated brownfield scorefor the portion of the geographical region based on the one or morecurrent physical locations of sites of the type of activity, theparticular location of the new site.
 22. The method of claim 19, furthercomprising assessing an impact of terminating the type of activity at aparticular one of the current physical locations of sites of the type ofactivity, comprising: determining an updated brownfield score for theportion of the geographical region based on (i) a set of physicallocations consisting of the one or more current physical locations ofsites of the type of activity other than the particular currentphysical.
 23. The method of claim 15, further comprising selecting thetiles from a larger set of tiles corresponding to the geographicalregion, comprising: selecting, from the larger set of tiles, one or morefirst tiles accessible via a street network, wherein each of the one ormore first tiles intersects a street in the street network; selecting,from the larger set of tiles, one or more second tiles adjacent to thestreet network, wherein each of the one or more second tiles borders atleast one of the first tiles; and including the one or more first tilesand the one or more second tiles in the plurality of tiles.
 24. Themethod of claim 23, wherein selecting the tiles further comprises:selecting, from the larger set of tiles, one or more tiles proximate toa governmental boundary; and including the one or more tiles proximateto the governmental boundary in the plurality of tiles.
 25. The methodof claim 15, wherein receiving the spatial relationship data for thetiles further comprises generating the spatial relationship data. 26.The method of claim 25, wherein generating the spatial relationship datacomprises: selecting pairs of tiles; and for each selected pair oftiles, determining a spatial relationship between central features ofthe respective pair of tiles.
 27. The method of claim 26, wherein thecentral features of the pairs of tiles comprise centroids of the pairsof tiles.
 28. A system comprising: one or more processing devices andone or more storage devices storing instructions, the processing devicebeing operable to execute the instructions to perform operationsincluding: receiving, from the data storage device, a dataset comprisingtile data corresponding to a geographical region, wherein the tile datacorresponds to respective sub-regions of the geographical region andtile attribute records, and wherein the tile data (i) uniquelyidentifies a respective tile and (ii) indicates respective sub-regionattribute values of the sub-region corresponding to the respective tile;receiving, from the data storage device, spatial relationship data,wherein the spatial relationship data (i) identifies a respective pairof tiles included in the tile data and (ii) indicates a spatialrelationship between the respective pair of tiles; spatiallyapportioning the tile data based at least in part on the spatialrelationship data by the processing device, wherein performing thespatially relative apportionment includes, for each tile T in the tiledata and each spatial relationship R between tiles, identifying, basedon the spatial relationship data, a subset of the tile data having therespective spatial relationship R to the respective tile T, for eachsub-region attribute, generating an aggregated attribute value for therespective attribute, generating a record of spatially relativeapportionment (i) identifying the respective tile T and the respectivespatial relationship R and (ii) indicating the aggregated attributevalues of the subset of tiles having the respective spatial relationshipR to the respective tile T.
 29. The system of claim 28, wherein thespatial relationship data is obtained prior to the performing of thespatially relative apportionment.
 30. The system of claim 28, whereinthe operations further include: generating a second set of tile data forthe of tiles; and the one or more processing devices being configured toperform spatially relative apportionment of the second set of tile databased on the spatial relationship data.
 31. The system of claim 28,wherein the operations further include assessing a quality of a set ofphysical locations of sites of a type of activity within a portion ofthe geographical region corresponding to a subset of the tiles.
 32. Thesystem of claim 31, wherein assessing the quality of the set of physicallocations comprises: identifying, within the spatially apportioned data,the records of spatially relative apportionment identifying the subsetof tiles corresponding to the portion of the geographical region;determining a greenfield benchmark score for the portion of thegeographical region based on one or more potential locations of sites ofthe type of activity within the geographical region and the identifiedrecords of spatially relative apportionment; determining a brownfieldscore for the portion of the geographical region based on one or morecurrent physical locations of sites of the type of activity within thegeographical region and the identified records of spatially relativeapportionment; and determining a quality score based on the brownfieldscore and the greenfield benchmark score.
 33. The system of claim 32,wherein determining the quality score comprises determining a ratio ofthe brownfield score to the greenfield benchmark score.
 34. The systemof claim 32, wherein the operations further include assessing an impactof adding a new site of the type of activity at a particular locationwithin the portion of the geographical region, comprising: determiningan updated brownfield score for the portion of the geographical regionbased on the one or more current physical locations of sites of the typeof activity, the particular location of the new site, and the identifiedrecords of spatially relative apportionment.
 35. The system of claim 32,wherein the operations further include assessing an impact ofterminating the type of activity at a particular one of the currentphysical locations of sites of the type of activity, comprising:determining an updated brownfield score for the portion of thegeographical region based on (i) a set of physical locations consistingof the one or more current physical locations of sites of the type ofactivity other than the particular current physical location and (ii)the identified records of spatially relative apportionment.